Monitoring vehicle diagnostic data available from ports like OBD-II/CAN, J1708/J1939, accelerometer, and GPS data over the wireless network is a known art. Current art can be divided into two groups:                1. Methodology 1:                    a. Collecting the vehicle diagnostic data onboard the vehicle,            b. sending the data to a server computer over the wireless network, and            c. analyzing the data at the server for determining the health condition of the vehicle, computing fuel economy, monitoring emissions and driver behavior,            d. presenting the results of the analysis to the user via a web link to the server.                        2. Methodology 2:                    a. Collecting the vehicle diagnostic data onboard the vehicle from the onboard diagnostics port and accelerometer,            b. Analyzing the data onboard the vehicle for determining the health condition of the vehicle, computing fuel economy, monitoring emissions and driver behavior,            c. throwing away the collected data,            d. sending the results generated by the onboard analysis to the remote server over the wireless network.            e. presenting the results of the analysis to the user via a web link to the server.                        
Note that the main difference between these two approaches is where the data analysis is performed. The location of the data analysis has a huge impact on the technology used since real-time onboard monitoring of vehicle performance data using resource constrained computing environment requires completely different sets of algorithms and business models (note that onboard analysis dramatically reduces the data communication cost).
Several examples of known art based on Methodology 1 exist. For instance, U.S. Pat. No. 5,499,182 is directed to multiple vehicle component sensors mounted to a host vehicle measure vehicle component parameters indicative of a host vehicle's driver performance. A microprocessor module detachably coupled to the vehicle mounting unit affixed to and uniquely designated for a given host vehicle poles each vehicle sensor of that host vehicle to read, process, and store the vehicle operation data generated thereby. A playback mounting unit at a remote computer connects the remote computer to the host vehicle's microprocessor module in order to establish digital communication whereby the vehicle operation data and the analysis results processed therein are retrieved and displayed for a user. In addition, the driver integrity-checking module is based on some pre-determined values of the parameters and is done remotely after the data is played back on the remote computer. Also, the vehicle needs to be mounted by a multiple number of sensors as opposed to using the standard OBDII data bus for getting the vehicle data in the subject vehicle driver performance system.
U.S. Pat. No. 5,207,095 is directed to an onboard vehicle computer system for use in evaluating an operator's braking technique that employs a plurality of vehicle-mounted sensors. The onboard computer in that system periodically receives and stores the parametric values associated with vehicle braking sensed by the sensors. The data thus generated by that computer is then available to be read later by an instructor who compares the recorded parametric values to formulate further instructive steps. That system does not perform any lightweight and sophisticated onboard data mining techniques on the data. Any evaluations to be made in light of the raw data are left for the user to make by themselves. Furthermore, as the vehicle sensor monitoring system there is intended specifically as an instructional tool, monitoring is performed only during those discrete time intervals related to an instructional session.
U.S. Pat. No. 6,609,051 is directed to a vehicle condition-monitoring system that employs machine learning and data mining technologies on data acquired from a plurality of vehicles in order to create models. Frequent acquisition of vehicle sensor and diagnostic data enables comparison with the created models to provide continuing analysis of the vehicle for repair, maintenance and diagnostics. The on-board diagnostic systems process sensor readings and diagnostic information of the vehicle Embedded Control System in order to detect defaults. The maintenance systems on-board the vehicle continuously process sensor readings to determine the condition of the vehicle systems, parts and lubricants (e.g., brake pad wear, battery quality, and oil quality). Off-board diagnostic systems acquire vehicle diagnostics and sensor data or control on-board diagnostics and testing functions. The system uses OEM proprietary or standardized interfaces, for example, OBD to connect to the vehicle. Physical connections link the vehicle and the workshop test equipment, with short-range wireless communication systems eventually replacing cable connections.
U.S. Pat. No. 6,330,499 directs itself to a vehicle diagnostic and health monitoring system that includes a client computer device within the vehicle, coupled to the vehicle's monitoring systems, for data management, remote session management and user interaction, a communication system, coupled to the client computer device, for providing remote communication of data including data derived from internal monitoring systems of the vehicle, and a remote service center including a vehicle data store, a server computer, a diagnostic engine, and a communicator for communicating the results of analysis of vehicle information to the client computer device via the communication system.
U.S. Pat. No. 5,034,894 directs itself to a self-diagnosis computer system onboard a motor vehicle wherein a plurality of detectors are mounted on that vehicle's engine to detect any aberrant operating conditions. Although the computer system there performs continual monitoring while the vehicle is in operation, no provision is made for the assessment of driver performance based on any sensed parameters.
Similarly, U.S. Pat. No. 5,074,144 is directed to an onboard vehicle computer system for monitoring vehicle performance. Various transducers for continually monitoring various vehicle parameters are employed in that system; however, comprehensive means for analyzing the measured vehicle parameters to characterize or assess driver performance, per se, are not provided.
Prior state-of-the-art is based on linear threshold-based techniques that belong to Methodology 1 allow relatively simple tasks such as detection of a feature value crossing a limit set a priori. Moreover as noted earlier, these techniques are applied after the data is uploaded to a remote desktop computer from the vehicle. For example, these techniques may check whether the driver crossed a specified speed limit. Unfortunately, these techniques are not capable of detecting linear and nonlinear complex driving patterns and they require an expensive process of transferring data to a remote monitoring station at a regular basis over the wireless network.
However, prior art, U.S. Pat. No. 7,715,961, is an exception that belongs to Methodology 2 and performs advanced statistical data analysis and modeling for detecting patterns from vehicle performance, driver behavior, fuel consumption and emissions data onboard the vehicle and sending the resulting analytics to the server over the wireless network. The current patent application is an improvement of the art reported in U.S. Pat. No. 7,715,961. The current work reports more advanced onboard vehicle data stream mining techniques and their applications in different business processes. The main differences are as follows:                1. Advanced data stream mining algorithms such as principal component analysis, clustering, anomaly detection, predictive modeling, classification using support vector machines, decision trees for analysis of the vehicle performance data onboard the vehicle.        2. Application of the onboard vehicle performance data mining technology for advanced fuel consumption modeling, emissions monitoring and smog test, driver behavior scoring, vehicle health scoring.        3. Application of the onboard vehicle performance data mining technology in a distributed environment comprised of multiple vehicles connected over wireless networks for insurance premium computation, vehicle-to-vehicle social networking, playing computer games, and adaptive placement of advertisement based on vehicle performance profile.        